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          <h1 class="post-title" itemprop="name headline">【第一章】Google工程师亲授 Tensorflow2.0－入门到进阶——Tensorflow2简介与环境搭建</h1>
        

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        <p>本文讲解《Google工程师亲授 Tensorflow2.0－入门到进阶》的第一章，TensorFlow2简介与开发环境的安装<br>视频地址：<a href="https://coding.imooc.com/class/chapter/344.html#Anchor" target="_blank" rel="noopener">https://coding.imooc.com/class/chapter/344.html#Anchor</a></p>
<a id="more"></a>

<h1 id="TensorFlow2简介"><a href="#TensorFlow2简介" class="headerlink" title="TensorFlow2简介"></a>TensorFlow2简介</h1><blockquote>
<p>TensorFlow2: google的开源软件库；采取数据流图(流程图)、用于数值计算；支持多种平台(GPU、CPU、移动设备)；最初用于深度学习，变得越来越通用</p>
</blockquote>
<p>数据流图：<br><img src="/blog/images/20190812063529495.jpg" alt="数据流图"></p>
<h2 id="TensorFlow2的发展史"><a href="#TensorFlow2的发展史" class="headerlink" title="TensorFlow2的发展史"></a>TensorFlow2的发展史</h2><ol>
<li>2015.11 发布 0.1</li>
<li>2017.2 发布 1.0</li>
<li>2019春 发布 2.0 alpha</li>
</ol>
<h2 id="TensorFlow1-X的主要特性与缺点"><a href="#TensorFlow1-X的主要特性与缺点" class="headerlink" title="TensorFlow1.X的主要特性与缺点"></a>TensorFlow1.X的主要特性与缺点</h2><h3 id="主要特性"><a href="#主要特性" class="headerlink" title="主要特性"></a>主要特性</h3><ol>
<li>XLA————提升训练速度58倍，可以再移动设备运行</li>
<li>引入更高级的API————tf.layers/tf.metrics/tf.losser/tf.keras</li>
<li>TensorFlow调试器</li>
<li>支持docker镜像，引入TensorFlow Serving服务</li>
</ol>
<h3 id="1-X的架构"><a href="#1-X的架构" class="headerlink" title="1.X的架构"></a>1.X的架构</h3><p><img src="/blog/images/20190812064530539.jpg" alt="1.X的架构"></p>
<h3 id="1-X的缺点"><a href="#1-X的缺点" class="headerlink" title="1.X的缺点"></a>1.X的缺点</h3><ol>
<li>调试困难</li>
<li>API混乱</li>
<li>入口困难，入了门依旧困难</li>
<li>大批研究人员转向PyTorch</li>
</ol>
<h2 id="TensorFlow2-X的主要特性与优点"><a href="#TensorFlow2-X的主要特性与优点" class="headerlink" title="TensorFlow2.X的主要特性与优点"></a>TensorFlow2.X的主要特性与优点</h2><h3 id="主要特性-1"><a href="#主要特性-1" class="headerlink" title="主要特性"></a>主要特性</h3><ol>
<li>使用tf.keras和eager mode进行更加简单的模型构建</li>
<li>鲁棒的跨平台模型部署</li>
<li>强大的研究实验————keras、自定义训练逻辑</li>
<li>清除不推荐使用的API和减少重复来简化API</li>
</ol>
<h3 id="2-X的架构"><a href="#2-X的架构" class="headerlink" title="2.X的架构"></a>2.X的架构</h3><p><img src="/blog/images/20190812065112888.jpg" alt="2.X的架构"></p>
<h3 id="2-X简化的模型开发流程"><a href="#2-X简化的模型开发流程" class="headerlink" title="2.X简化的模型开发流程"></a>2.X简化的模型开发流程</h3><ol>
<li>使用tf.data加载数据</li>
<li>使用tf.keras构建模型，也可以使用premade estimator来验证模型。使用tensorflow hub进行迁移学习</li>
<li>使用eager mode进行运行和调试</li>
<li>使用分发策略来进行分布式训练</li>
<li>导出到SavedModel</li>
<li>使用TensorFLow Serve、TensorFlow Lite、TensorFlow JS部署模型</li>
</ol>
<h3 id="2-X的优点"><a href="#2-X的优点" class="headerlink" title="2.X的优点"></a>2.X的优点</h3><ol>
<li>easy to use</li>
<li>高度的灵活性</li>
<li>真正的可移植性</li>
<li>产品和科研结合</li>
<li>自动求微分</li>
<li>多语言支持</li>
<li>性能最优化</li>
<li>TensorFlow使用率最高<br><img src="/blog/images/20190808061532311.jpg" alt="实现效果"></li>
</ol>
<h2 id="TensorFlow与PyTorch"><a href="#TensorFlow与PyTorch" class="headerlink" title="TensorFlow与PyTorch"></a>TensorFlow与PyTorch</h2><ol>
<li>入门时间<pre><code>1.X 静态图
2.X 动态图
PyTorch  动态图</code></pre></li>
<li>图创建和调试</li>
<li>全面性</li>
<li></li>
</ol>
<h2 id="学习建议"><a href="#学习建议" class="headerlink" title="学习建议"></a>学习建议</h2><ol>
<li>忘掉TensorFlow1.x吧</li>
<li>PyTorch和TensorFlow2选一主修（二者都要掌握）</li>
<li>Keras逐渐淡出(已被google收购)</li>
</ol>
<h2 id="为什么使用TensorFlow"><a href="#为什么使用TensorFlow" class="headerlink" title="为什么使用TensorFlow"></a>为什么使用TensorFlow</h2><ol>
<li>GPU加速</li>
<li>自动求导</li>
<li>神经网络API</li>
<li>深度学习迅猛发展，2.0发布，机遇在手</li>
</ol>
<h1 id="课程目标"><a href="#课程目标" class="headerlink" title="课程目标"></a>课程目标</h1><ol>
<li>灵活掌握使用TensorFlow框架的能力</li>
<li>掌握相关的机器学习、深度学习的理论知识</li>
<li>独立开发项目，掌握大厂一线编程经验</li>
<li>达到初级深度学习算法工程师/研究者的水平</li>
</ol>
<h2 id="课程章节"><a href="#课程章节" class="headerlink" title="课程章节"></a>课程章节</h2><p><img src="/blog/images/20190812061848222.jpg" alt="课程章节"></p>
<h1 id="本地安装Tensorflow2-0"><a href="#本地安装Tensorflow2-0" class="headerlink" title="本地安装Tensorflow2.0"></a>本地安装Tensorflow2.0</h1><ol>
<li>安装Anaconda</li>
<li>在Anaconda中创建虚拟环境<br><img src="/blog/images/20190812071021260.jpg" alt="安装 Tensorflow 2.X"></li>
<li>在Anaconda虚拟环境中安装 Tensorflow 2.X<br><img src="/blog/images/20190812122756526.jpg" alt="安装 Tensorflow 2.X"></li>
</ol>
<p>安装gpu版本的命令</p>
<figure class="highlight lsl"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install tensorflow-gpu==<span class="number">2.0</span><span class="number">.0</span>-alpha0</span><br></pre></td></tr></table></figure>

<p>安装cpu版本的命令</p>
<figure class="highlight lsl"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install tensorflow==<span class="number">2.0</span><span class="number">.0</span>-alpha0</span><br></pre></td></tr></table></figure>

<p><img src="/blog/images/20190812122552702.jpg" alt="安装 Tensorflow 2.X"></p>
<ol start="4">
<li><p>安装完成<br><img src="/blog/images/20190812122922550.jpg" alt="安装 Tensorflow 2.X"></p>
</li>
<li><p>测试<br>在Anaconda中启动Notebook，输入如下命令</p>
<figure class="highlight vim"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">import tensorflow <span class="keyword">as</span> <span class="keyword">tf</span></span><br><span class="line"><span class="keyword">print</span>(<span class="keyword">tf</span>.__version__)</span><br></pre></td></tr></table></figure>

</li>
</ol>
<p><img src="/blog/images/20190812125250087.jpg" alt="安装 Tensorflow 2.X"></p>
<ol start="6">
<li>常见错误<br>如果出现如下的警告：<blockquote>
<p>FutureWarning: Passing (type, 1) or ‘1type’ as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / ‘(1,)type’.</p>
</blockquote>
</li>
</ol>
<figure class="highlight crystal"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:523: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  _np_qint8 = np.dtype([(<span class="string">"qint8"</span>, np.int8, <span class="number">1</span>)])</span><br><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:524: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  _np_quint8 = np.dtype([(<span class="string">"quint8"</span>, np.uint8, <span class="number">1</span>)])</span><br><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:525: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  _np_qint16 = np.dtype([(<span class="string">"qint16"</span>, np.int16, <span class="number">1</span>)])</span><br><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:526: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  _np_quint16 = np.dtype([(<span class="string">"quint16"</span>, np.uint16, <span class="number">1</span>)])</span><br><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:527: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  _np_qint32 = np.dtype([(<span class="string">"qint32"</span>, np.int32, <span class="number">1</span>)])</span><br><span class="line"><span class="symbol">D:</span>\Anaconda3\envs\Tersorflow2.x+Python3.<span class="number">6</span>\<span class="class"><span class="keyword">lib</span>\<span class="title">site</span>-<span class="title">packages</span>\<span class="title">tensorflow</span>\<span class="title">python</span>\<span class="title">framework</span>\<span class="title">dtypes</span>.<span class="title">py</span>:532: <span class="title">FutureWarning</span>: <span class="title">Passing</span> (<span class="title">type</span>, 1) <span class="title">or</span> '1<span class="title">type</span>' <span class="title">as</span> <span class="title">a</span> <span class="title">synonym</span> <span class="title">of</span> <span class="title">type</span> <span class="title">is</span> <span class="title">deprecated</span>;</span> in a future version <span class="keyword">of</span> numpy, it will be understood <span class="keyword">as</span> (<span class="keyword">type</span>, (<span class="number">1</span>,)) / <span class="string">'(1,)type'</span>.</span><br><span class="line">  np_resource = np.dtype([(<span class="string">"resource"</span>, np.ubyte, <span class="number">1</span>)])</span><br></pre></td></tr></table></figure>

<p>这是因为是numpy版本过高，可以使用如下命令将numpy降级：</p>
<figure class="highlight lsl"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">pip install -U numpy==<span class="number">1.16</span><span class="number">.0</span></span><br></pre></td></tr></table></figure>

<p><img src="/blog/images/20190812125555720.jpg" alt="将numpy降级"></p>
<p>之后再导入tensorflow的时候就不会有这样的警告了。<br><img src="/blog/images/20190812130743027.jpg" alt="将numpy降级"></p>

      
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                    resultItem += "<li><a href='" + articleUrl + "' class='search-result-title'>" + title + "</a>";
                  }

                  slicesOfContent.forEach(function (slice) {
                    resultItem += "<a href='" + articleUrl + "'>" +
                      "<p class=\"search-result\">" + highlightKeyword(content, slice) +
                      "...</p>" + "</a>";
                  });

                  resultItem += "</li>";
                  resultItems.push({
                    item: resultItem,
                    searchTextCount: searchTextCount,
                    hitCount: hitCount,
                    id: resultItems.length
                  });
                }
              })
            };
            if (keywords.length === 1 && keywords[0] === "") {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-search fa-5x" /></div>'
            } else if (resultItems.length === 0) {
              resultContent.innerHTML = '<div id="no-result"><i class="fa fa-frown-o fa-5x" /></div>'
            } else {
              resultItems.sort(function (resultLeft, resultRight) {
                if (resultLeft.searchTextCount !== resultRight.searchTextCount) {
                  return resultRight.searchTextCount - resultLeft.searchTextCount;
                } else if (resultLeft.hitCount !== resultRight.hitCount) {
                  return resultRight.hitCount - resultLeft.hitCount;
                } else {
                  return resultRight.id - resultLeft.id;
                }
              });
              var searchResultList = '<ul class=\"search-result-list\">';
              resultItems.forEach(function (result) {
                searchResultList += result.item;
              })
              searchResultList += "</ul>";
              resultContent.innerHTML = searchResultList;
            }
          }

          if ('auto' === 'auto') {
            input.addEventListener('input', inputEventFunction);
          } else {
            $('.search-icon').click(inputEventFunction);
            input.addEventListener('keypress', function (event) {
              if (event.keyCode === 13) {
                inputEventFunction();
              }
            });
          }

          // remove loading animation
          $(".local-search-pop-overlay").remove();
          $('body').css('overflow', '');

          proceedsearch();
        }
      });
    }

    // handle and trigger popup window;
    $('.popup-trigger').click(function(e) {
      e.stopPropagation();
      if (isfetched === false) {
        searchFunc(path, 'local-search-input', 'local-search-result');
      } else {
        proceedsearch();
      };
    });

    $('.popup-btn-close').click(onPopupClose);
    $('.popup').click(function(e){
      e.stopPropagation();
    });
    $(document).on('keyup', function (event) {
      var shouldDismissSearchPopup = event.which === 27 &&
        $('.search-popup').is(':visible');
      if (shouldDismissSearchPopup) {
        onPopupClose();
      }
    });
  </script>





  

  

  

  
  

  

  

  

</body>
</html>
